testováno na čisté instalaci systému Fedora 31
Pro ty, kteří vědí, co dělají je zde příkaz pro instalaci všech balíčků v jedné dávce. Ostatním doporučuji projít si kroky níže.
sudo dnf -y install texlive texstudio texlive-babel-czech texlive-acronym texlive-engrec texlive-dirtree texlive-sectsty texlive-greek-fontenc texlive-cbfonts texlive-collection-langczechslovak texlive-epstopdf && sudo fmtutil-sys --all && sudo texhash
👉 ipatool is an open-source tool developed by Majd, a highly trustworthy and talented developer in the iOS community. Recently, ipatool got a significant update that allows users to easily download older versions of iOS apps on macOS/Windows/Linux!.
👉 Since ipatool doesn't have a graphical user interface (GUI), some of you might think it's tricky to use. But trust me, it's not! Here's a simple guide if you're still a bit scared of the terminal. (Tbh, everything in this *guide can be found on ipatool's repo)
👉 Note: You need to log into your Apple ID via ipatool for the tool to work. Unless you prioritize security above all, you can trust logging into your Apple account with ipatool. As explained earlier, it’s an open-source tool developed by a well-known and reliable developer, minimizing security risks to the lowest level.
A quick DevTools snippet to extract clean, readable text from documentation pages (Chrome Docs, React, Python, etc.) directly into your clipboard or file.
Removes navigation bars, sidebars, and HTML clutter—giving LLMs (ChatGPT, Claude, Gemini) pure text and code examples without wasting context tokens.
F12 or Ctrl + Shift + I / Cmd + Option + I).
Some notes and techniques for reverse engineering Webpack (and a little bit about React/Vue/Angular) apps.
| #!/usr/bin/env bash | |
| # Download VMware Workstation for Linux | |
| # Handles legacy versions (12.x - 17.6.3) from Cloudflare CDN cache and | |
| # newer versions (17.6.4+) from Archive.org. | |
| # | |
| # Use '-k' to keep the CDN download file compressed, exiting after download. (Ignored for v17.6.4+) | |
| # Use '-v <Version>' to specify desired version directly. | |
| BASE_URL="https://softwareupdate-prod.broadcom.com/cds/vmw-desktop/" |
| {:epupp/script-name "pez/page_choices.cljs" | |
| :epupp/auto-run-match "*" | |
| :epupp/description "See this page's Statsig experiments, and change the ones this visit is in." | |
| :epupp/run-at "document-start" | |
| :epupp/inject ["scittle://replicant.js" | |
| "epupp://epupp/ui.cljs"]} | |
| (ns pez.page-choices | |
| (:require [clojure.string :as string] | |
| [clojure.walk :as walk] |
| //////////////////////////////////////////////////////////////////////// | |
| // Warcraft III Custom Grid Hotkeys | |
| // | |
| // Maps the 4x3 command card to: QWER / ASDF / ZXCV | |
| // All unit and hero abilities use QWER. | |
| // Attack: A Stop: S Move: Z | |
| // Recommended inventory grid: TY / GH / BN (set these in-game). | |
| // | |
| // Download: On GitHub, click Raw, then Save As (Ctrl+S). Save the file | |
| // as CustomKeys.txt and make sure it is not named CustomKeys.txt.txt. |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, a persistent memory engine for AI coding agents.
This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.
The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.